Cell Cycle Phases, Spindle Dynamics and Kinesin-5 Motor Localization Characterized by Deep Learning, Dual Segmentation and Decision-Tree Pipeline

Cell size reduction distinctly scales spindle elongation and chromosome segregation in C. elegans

How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2- to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation with large-scale electron tomography, we find that mid-spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces spindle pole-to-pole dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo, spindle-length scaling across nematode species spanning ~100 million years of divergence, and spindle rotation in human cells. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species. Embryos must adapt to decreasing cell size. Although chromosome segregation. dynamics in C. elegans remain largely invariant, spindle length scales with cell. size and can be recapitulated by only varying cell size and microtubule catastrophe. rates.

FOCUS-3D: Robust, generalizable volumetric cell segmentation for three-dimensional fluorescence microscopy

Understanding how cells establish spatial organization within tissues is a fundamental question in life sciences. While modern three-dimensional fluorescence microscopy captures large-volume tissue architecture, extracting quantitative cellular insights from complex volumetric datasets remains a major barrier. Here, we introduce FOCUS-3D, a robust, broadly generalizable volumetric cell segmentation framework built on a large, diverse manually annotated cell resource and advanced AI designs. Integrating volumetric representation learning, multi-scale feature extraction, and query-based mask prediction, FOCUS-3D achieves state-of-the-art performance across diverse species, tissues, fluorescent reporters and imaging modalities. During zebrafish (Danio rerio) development, FOCUS-3D uncovers three successive phases of notochord morphogenesis. We disentangle early motility-driven rearrangements from later cell shape remodeling and tissue repacking, and further link these morphological states to spatial and developmental transcriptional programs across independent datasets.

Live single-molecule imaging reveals global shifts in mRNA mobility during human stem cell differentiation

Spatiotemporal regulation of mRNA localisation is fundamental to cell identity specification and function, yet tracking transcript dynamics in living differentiating cells remains technically challenging. Here, we establish a robust pipeline for MS2 tagging of endogenous transcripts in human induced pluripotent stem cells (iPSCs), coupled with single-particle tracking and Hidden Markov Modelling to map mRNA mobility landscapes during differentiation and cell state transitions. Applying this approach to different cytoskeleton-encoding transcripts in diverse contexts — neural organoids, directly programmed neurons, and vascular organoids — we reveal a conserved principle: Both, β-actin and β2b-tubulin particle dynamics progressively shift towards constrained, compartmentalised patterns as cells acquire cell type identity. Perturbation experiments demonstrate that microtubule-dependent tethering is a common, conserved mechanism controlling β-actin mRNA localisation in all cell types studied, whereas translation-dependent anchoring and actin filaments contribute in a context-dependent manner. Analysis of particle dynamics in migrating blood vessel progenitors further showed that β-actin mRNAs accumulating at cell edges are highly diffusive, while those in perinuclear regions show constrained movement. Together, our integrated framework provides a scalable foundation for mechanistic dissection of mRNA targeting in human developmental and disease models.

Bachelor Computer Science & Bioinformatics Improving Early S Phase Detection in Hoechst-Based Cell-Cycle Segmentation Using Temporal Context

CellColoc: A modular, open-source workflow for cell colocalization, segmentation, and feature extraction in microscopy images

Quantitative cell colocalization in fluorescence microscopy often depends on ad hoc combinations of image loading, segmentation, region selection, manual inspection, and spreadsheet post-processing. Such workflows are difficult to transfer across projects and often obscure how intermediate results were produced. We present CellColoc, an open-source Python workflow pipeline for segmentation-based cell colocalization, single-channel segmentation, and cell feature extraction in 2D and 3D microscopy images. CellColoc provides a modular workflow layer that integrates existing segmentation backends, including Cellpose and threshold-based methods, into reusable, script-driven analyses. The package supports channel-wise backend selection, interactive or reusable regions of interest, optional third-channel occupancy and cell-positivity analysis, z-cropping and z-projection, cached post hoc refinement of Cellpose thresholds, and reanalysis after manual mask editing. Analyses are executed from concise user scripts while reusable functionality is kept in a core package. Intermediate artifacts such as ROI masks, per-channel label masks, positive-cell masks, and structured result tables are written to a standardized results directory, promoting transparent inspection, reproducibility, and FAIR-aligned reuse. Public example datasets, a synthetic benchmark, and archived software releases accompany the package. By separating reusable analysis logic from project-specific configuration, CellColoc offers an extensible foundation for community-driven microscopy workflows that need transparent per-cell overlap classification, morphology readouts, and reusable batch analysis.

Higher-Order Cell Tracking Transformer

Reconstructing lineages from live-imaging microscopy requires linking cell detections across time, including through cell divisions. A common approach is to construct a candidate graph and associate cell segmentations (nodes) across frames. However, these and other existing methods overlook two structural obstacles in candidate tracking graphs: (i) cell divisions entangle distinct lineage paths in the node embedding space, and (ii) edges sharing a node have near-random label agreement, so the candidate-graph topology carries no useful information for graph neural networks to aggregate. We propose the \textbf{Higher-Order Cell Tracking Transformer} (HOCT), an edge-centric architecture in which candidate cell links attend to one another under a 3D geometric prior, resolving both issues. Evaluated on the Cell Tracking Challenge and a bacteria division benchmark, HOCT achieves state-of-the-art results without deep pre-trained image encoders. Moreover, the proposed approach is easier to fine-tune, quickly reducing tracking errors by 59% with 400 annotations in a human-in-the-loop setting, outperforming LoRA fine-tuning of competing transformer baselines (6.75% improvement).